Maximizing Your SPARC t4 Oracle Solaris Application Performance


Maximizing Your SPARC T4 Oracle Solaris Application Performance§ Darryl Gove

Senior Principal Software Engineer


Program Agenda

§ Hardware§ Correctness§ Performance§ Parallelism

Copyright © 2012, Oracle and/or its affiliates. All rights reserved.4

More Information§ Download, technical articles and more: http://oracle.com/goto/solarisstudio

OpenWorld Sessions

§ Mon, Oct 1, 10:45 - 11:45 AM: Maximizing Your SPARC T4 Oracle Solaris Application Performance, CON 6382 (Marriott Marquis - Golden Gate)

§ Mon, Oct 1, 3:15 - 4:15 PM: Technical Panel: Developing High Performance Applications on Oracle Solaris, CON 7196 (Marriott Marquis - Golden Gate)

Hands-on Lab

§ Wed, Oct 3, 1:15 - 2:15 PM: Develop C/C++ Applications for the Cloud with Oracle Tuxedo and Oracle Solaris Studio, HOL 10276 (Marriott Marquis - Salon 5/6)

JavaOne Sessions

§ Mon, Oct 1, 8:30 – 9:30 AM: Mixed-Language Development: Leveraging Native Code from Java, CON 6714 (Hilton San Francisco -Continental Ballroom 6)

§ Tues, Oct 2, 1:00 – 2:00 PM: Take Performance Tuning of Your Enterprise Java Applications to the Next Level , CON 10213 (Hilton San Francisco -Continental Ballroom 6)

http://oracle.com/goto/solarisstudio


Oracle Solaris Studio

© 2011 Oracle Corporation – Proprietary and Confidential 4

Performance Analyzer provides unparalleled insight into your app, allowing you to identify bottlenecks and improve performance by orders of magnitude

Code Analyzer ensures app reliability by detecting app vulnerabilities, including memory leaks and memory access violations

Thread Analyzer simplifies complex parallel programming errors by detecting hard to pinpoint race and deadlock conditions

Integrated Development Environment increases developer efficiency

New

Analysis Suite

C, C++ Compilers utilize advanced code generation technology to optimize apps for highest performance on SPARC & x86

Fortran Compiler optimizes compute intensive app performance

Debugger ensures app stability with event handling & multi-thread support

Performance Library maximizes compute-intensive app performance using advanced numeric solver libraries

Compiler Suite


Oracle Solaris Studio 12.3 Highlights

Accelerate Performance

Ø 3x faster code on SPARC T4 than GCC; 40% faster than Sun Studio 12

Ø 1.5x faster code on Intel x86 than GCC; 20% faster than Sun Studio 12

Gain Extreme Observability

Ø New Code Analyzer for more reliable applications; reports common coding & memory access errors faster than competitive alternatives

Ø Enhanced Performance Analyzer with system-wide performance analysis

Improve Productivity

Ø Remote access to Solaris Studio tools from local desktop (Oracle Solaris, Linux, Microsoft Windows, Mac)

Ø Streamlined Oracle DB application developmentØ Simplify Oracle Tuxedo development with IDE plug-inØ IPS distribution on Solaris 11 for simplified managementØ 20% faster compile time


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SPARC T4 Hardware


SPARC T4 - Overview

§ Not like T1 – T3 (only shares the T-series name)§ Single thread performance § Multithread throughput


SPARC T4 - Details

§ 1 to 4 chips per system§ 8 cores per chip

● Dual issue

● Out-of-order

§ 8 threads per core§ 3.0 GHz clock

● 48B (3.0GHz * 8 * 2) instructions / sec / chip


SPARC T4 - Capacity

§ Chip capacity: 48 B instructions / sec§ For fully active threads:

● Single thread: 6 B instructions / sec

● Each of eight threads: 0.75 B instructions / sec

§ Threads rarely fully active:● I/O wait

● Processor stall (fetch from memory = 300-400 cycles)


Developing for T4

§ Make it correct§ Remove obvious performance issues§ Make it scale (correctly)



Application Correctness


Debug information

§ Always use -g§ No optimisation flags:

● Full debug

● Lower performance

§ Optimised binaries:● Best effort debug

● No/minimal performance impact

§ Debug what you ship!


Automatic Error Detection

§ Static/compile time error detection● Code Analyzer

§ Dynamic/runtime memory access error detection● Discover


Code Analyzer

§ Static analysis for common coding errors● Uninitialised variables, etc.

§ Compile with:● -xanalyze=code

§ View results with:● code-analyzer <a.out>


Code Analyzer – example output


Memory Error Detection - discover

§ Common memory allocation and use errors:● Uninitialised memory

● Access past bounds

● Memory leaks

§ Usage: ● discover <a.out>

● <a.out>

● Default = html output


Example of discover$ ./a.outERROR 1 (ABR): reading memory beyond array bounds at address 0xffbff278 (8 bytes) on the stack at: average() + 0x228 <disc.c:8> 6: for (int i=1; i<=len; i++) 7: { 8:=> total+=array[i]; 9: } _start() + 0xd8 ... double array[20]; ... printf(" Average = %f\n", average(array,20) );



Application Performance


Optimisation – the Basics

§ No optimisation flags == no optimisation§ Good optimisation: -O§ Advanced optimisations:

● Guided by profile of appliaction

● Knowledge of deployment systems


Profiling

§ Profiling with the performance analyzer● collect <a.out>

● collect -P <pid>

● analyzer test.1.er

§ Report generation with spot● spot <a.out>

● spot -P <pid>


Performance Analyzer

§ Demo


Aggressive Optimisation

§ One stop flag: -fast§ Enables multiple optimisations

● Build machine = deployment machine

● Floating point simplification and optimisation

● Pointers to different types do not alias

● Function inlining

§ Investigate performance gain


Profile Drives Flag Selection Floating Point§ Significant time in floating point computation:

● Floating point simplification

● -fsimple=2

§ Significant time in floating point library code:

● Optimised floating point libraries

● -xlibmopt, -xlibmil

§ Use FP optimisations if performance improves and FP optimisations are acceptable


Profile Drives Flag Selection Flat profile§ Many hot small functions

● At least -xO4 optimisation level

● -xipo for cross-file optimisations

§ Conditional code or inlining

● Profile feedback

● -xprofile=collect:

● Training run of application

● -xprofile=use:


Profile Drives Flag Selection Pointers§ Pointers inhibit compiler optimisations§ Compiler needs more information§ restrict qualified pointers in C

● Localised action

§ Flags:● -xrestrict (restrict qualified pointers passed into functions)

● -xalias_level=std [C]

● -xalias_level=compatible [C++]

● Actions at file level


Processor Specific Optimisations

§ Default: -xtarget=generic often good enough

§ T4 has useful instructions● Compare and branch

● Floating point multiply add

§ One stop flag: -xtarget=T4§ Schedules for T4, uses entire T4 instruction set§ Only runs on T4 (or later) processors


SPARC Instruction Sets



Multi-threaded Applications


Multi-thread or Multi-process

§ Multiprocess:● Isolation

● Independence

● Large virtual memory footprint

● Potentially high synchronisation costs

§ Multithread

● Low synchronisation costs

● Minimal memory footprint

Throughput

Latency


Multi-threaded Application Development

§ POSIX threads (C11, C++11)● Low level: Great control, significant complexity

§ OpenMP

● High abstraction: Easy to use, flexible

§ Automatic parallelisation

● Trivial to use: -xautopar -xreduction

● Works best for loop-intensive code (typically FP)


OpenMP Parallel For

§ Distributes iterations across CPUs

#pragma omp parallel for

for (int i=0; i<length; i++)

{

// Do work

}


OpenMP Tasks

§ Distributes work across CPUs


{

#pragma omp task

{

// Do work for task ‘i’

}

}


Parallel Program Correctness

§ Distributes work across CPUs

int total=0;

#pragma omp parallel for


{

total += i;

}§ Data race: Multiple threads updating the same variable


Thread Analyzer

§ Instrument application● Compiler flag: -xinstrument=datarace

● Binary instrumentation: discover -i datarace <a.out>

§ Gather data:● collect -r on <a.out>

§ View data:

● tha tha.1.er


Thread Analyzer - Example

§ Demo


Scaling to Many Threads

§ Minimise serial code● Amdahl’s Law

§ Minimise lock contention§ Minimise writes of shared data§ Evenly distribute work


Scaling to Many Threads

§ Demo


Limits of Performance

§ Threads● vmstat

§ Instruction Issue Width

● pgstat / cputrack / cpustat / ripc

§ Bandwidth

● busstat / bw


Conclusion: Optimising for T4

§ Step 1: Profile and remove inefficient code§ Step 2: Explore benefits of increased optimisation§ Step 3: Identify opportunities for parallelisation§ Step 4: Profile and tune parallel code § Step 5: Watch for hitting hardware limits

Maximizing Your SPARC t4 Oracle Solaris Application Performance

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